Papers › YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos
YouMakeup VQA Challenge: Towards Fine-grained Action Understanding in Domain-Specific Videos
Shizhe Chen, Weiying Wang, Ludan Ruan, Linli Yao, Qin Jin
The goal of the YouMakeup VQA Challenge 2020 is to provide a common benchmark for fine-grained action understanding in domain-specific videos e.g. makeup instructional videos. We propose two novel question-answering tasks to evaluate models' fine-grained action understanding abilities. The first task is \textbf{Facial Image Ordering}, which aims to understand visual effects of different actions expressed in natural language to the facial object. The second task is \textbf{Step Ordering}, which aims to measure cross-modal semantic alignments between untrimmed videos and multi-sentence texts. In this paper, we present the challenge guidelines, the dataset used, and performances of baseline models on the two proposed tasks. The baseline codes and models are released at \url{https://github.com/AIM3-RUC/YouMakeup_Baseline}.
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